基于信息融合技术的矿井通风故障诊断  

Mine Ventilation Fault Diagnosis Based on Information Fusion Technology

作　　者：杨勇 Yang Yong(Shanxi Coal Transportation and Sales Group Jinxinda Coal Co.,Ltd.,Linfen Shanxi 041000)

机构地区：[1]山西煤炭运销集团金辛达煤业有限公司,山西临汾041000

出　　处：《机械管理开发》2022年第10期143-145,共3页Mechanical Management and Development

摘　　要：为提高故障诊断的准确性,提出了一种多故障特征信息融合的故障诊断方法。该方法的参考信息包括定子电流信号、轴向振动信号和径向振动信号。对采集到的信号进行小波分析,提取出故障小波分析方法。基于每种类型的信息,通过神经网络计算得到初步的结论。为了得到最终结论,采用dempster-shafer evidence theory(以下简称D-S证据理论)组合规则实现信息融合。实验结果表明,采用信息融合技术处理的故障诊断信号进行分析,得出的结论可靠性明显提高。In order to improve the accuracy of fault diagnosis, a fault diagnosis method with the fusion of multiple fault feature information is proposed. The reference information of the method includes stator current signal, axial vibration signal and radial vibration signal.Wavelet analysis is performed on the collected signals to extract the fault wavelet analysis method. Based on each type of information,preliminary conclusions are obtained through neural network calculations. In order to obtain the final conclusions, information fusion was achieved using the combined rules of dempster-shafer theory(hereafter referred to as D-S evidence theory). The experimental results show that the reliability of the conclusions drawn from the analysis of the fault diagnosis signals processed using the information fusion technique is significantly increased.

关 键 词：矿井通风机 证据理论 信息融合 故障诊断 

分 类 号：TD63[矿业工程—矿山机电]